A Graph3-based method for monitoring power data

Abstract

This invention discloses a power data monitoring method based on Graph3, comprising: 1. Statistically analyzing the start and end code data details for each day based on historical data of the power meter base codes over 7 days, and marking and displaying questionable data. 2. Calculating the daily power generation data based on the obtained base code values ​​for each day. 3. Determining whether the power link status at the key points is normal based on snapshot data from the power meter measuring points, and displaying the status changes through a trend chart. 4. Comparing and displaying the changes in power data of different generating units within a statistical period using a bar chart. This invention can effectively utilize existing real-time acquired measurement data to monitor the power communication link status and statistically analyze power data over 7 days.

Description

Technical Field

[0001] This invention belongs to the field of information technology in the energy and power industry, and specifically relates to a method for monitoring electricity data based on Graph3. Background Technology

[0002] Electricity consumption data has always been one of the most important statistical indicators in the power industry, so the accuracy of electricity meter readings and statistical results must be guaranteed. The electricity meter readings at midnight each day are not only the basis for daily electricity consumption statistics but also related to the statistics of long-term data such as monthly and annual cumulative values. Therefore, to ensure the continuity and accuracy of electricity consumption data, a clearer monitoring interface needs to be redesigned to enable online monitoring of electricity consumption data. Summary of the Invention

[0003] To overcome the above technical problems, this invention provides a power data monitoring method based on Graph3. This method effectively utilizes existing real-time collected measurement data to monitor the power communication link status and perform statistical analysis of power data within 7 days under Graph3.

[0004] To achieve the above objectives, the present invention employs the following technical solution:

[0005] A method for monitoring power data based on Graph3 includes the following steps:

[0006] Step 1: Define a set of power meter start base code measurement points A1, A2…A7, a set of power meter end base code measurement points B1, B2…B7, and a set of power meter statistical result measurement points C1, C2…C7 for each power indicator, to monitor detailed power information within 7 days before 0:00 on the current day; define a gateway power communication link status measurement point R1 to monitor changes in link status.

[0007] Step 2: Using 0:00 T0 as the baseline, calculate backwards for 7 days to obtain a time series T1, T2…T7; using (T1,T0), (T2,T1)….(T7,T6) as statistical periods, read the historical data of the 7-day electricity meter readings, and then obtain the first data (L1,L2…L7) and the last data (K1,K2…K7) for each time period. Write (L1,L2…L7) as a data snapshot to (A1,A2…A7), and write (K1,K2…K7) as a data snapshot to (B1,B2…B7). The actual electricity consumption calculation expression is C. i =(K i -L i )*F, where K represents the last data in the statistical period, L represents the first data in the statistical period, the subscript i takes values ​​of 1, 2, ..., 7 to indicate the day, F is the electricity meter multiplier, Ci Unit: 10,000 kWh;

[0008] Step 3: Obtain snapshots S1 and S2 of the power meter measurement points to get snapshot times M1 and M2; then compare the snapshot times M1 and M2 with the current system time M0. If the deviation between the two is within N0, where N0 is the set time deviation, it is judged that the link state of the gateway power is normal; otherwise, it is judged that the link state of the gateway power is interrupted.

[0009] Step 4: In the Graph3 display page, the power data monitoring area for one week is used to display the power details within 7 days. Compare the meter reading bottom code data with the page bottom code data to check whether the collected data or the meter reading data is incorrect; if the bottom code time of a certain day is not 00:00:00, it is considered that the bottom code data and the statistical result at that moment are doubtful, and the time is displayed in red font and flashes to remind attention; after comparison, check whether the collected and calculated data is consistent with the site, and then modify according to the actual situation to ensure the accuracy of the data source referenced by function modules such as reports.

[0010] A further improvement of the present invention is that in step 2, first obtain the current system time T, then calculate the time T0 at 0:00:00 on the same day, and推算 7 days forward with a 1-day cycle to get a time series T1, T2... T7. Respectively use (T1, T0), (T2, T1)... (T7, T6) as the statistical periods, and call the function of taking historical data to read the sample values within each time period.

[0011] A further improvement of the present invention is that for the first sample value within each time period, write it into the snapshot of the corresponding measurement point with the time corresponding to the sample value as the snapshot time, and for the last sample value within each time period, write it into the snapshot of the corresponding measurement point with the time corresponding to the sample value as the snapshot time.

[0012] A further improvement of the present invention is that according to the calculation expression of the actual power, calculate the data within each statistical period, and use the statistical end time minus 1 second as the snapshot time to write it into the snapshot of the corresponding measurement point.

[0013] A further improvement of the present invention is that in step 3, the judgment rule is as follows:

[0014] R1 = (M1 - M0) < N0 or (M2 - M0) < N0, where N0 is the set time deviation, and finally write the judgment status into the real-time database.

[0015] A further improvement of this invention is that, in step 4, the link status monitoring area is used to track the changes in link status from the previous 7 days to the current time. By observing the changes in trends, it is possible to make a preliminary judgment on whether the power data within 7 days is valid, and on the other hand, it is possible to detect situations where data collection is interrupted, so as to remind on-site personnel to handle the situation in a timely manner, restore communication, and ensure the continuity of data collection.

[0016] A further improvement of this invention is that, in step 4, the weekly power generation comparison area is displayed in the form of a bar chart in ascending order of time, showing the power generation statistics of the two units within 7 days, which can intuitively reflect the actual power generation situation within these 7 days.

[0017] The present invention has at least the following beneficial technical effects:

[0018] 1. Compared to the single method of only displaying the current base code value on the SIS page, this invention displays in detail the starting base code, ending base code, and corresponding start and end times for each day of the electricity meter within a week, so as to compare the automatic collection results with the on-site meter reading base code data.

[0019] 2. By tracking the status of the power link at the gateway in real time using a trend line, users can quickly detect whether the communication link is abnormal from the changes in the trend line status, and thus arrange for maintenance personnel to check the link as soon as possible and restore data communication, thereby improving the continuity of basic data.

[0020] 3. The display page allows configuration of multiple generator set base code measurement points, enabling a direct comparison of the power generation of different generator sets within a week.

[0021] 4. The display page can not only statistically analyze power generation data, but can also be expanded to include power generation data from other key points or auxiliary equipment power generation data. Attached Figure Description

[0022] Figure 1 This is a diagram illustrating the effect of the method of the present invention.

[0023] Figure 2 This is a diagram showing the comparison of electricity consumption over a week using a bar chart control. Detailed Implementation

[0024] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0025] This invention provides a power data monitoring method based on Graph3, comprising the following steps:

[0026] Step 1: Add a memory driver in RDBProxy, defining the starting base code measurement points A1, A2…A7, the ending base code measurement points B1, B2…B7, and the statistical result measurement points C1, C2…C7. The memory driver tag is used here to display the battery meter base code details and statistical results on the page. There is no write-back operation to the real-time database, so there is no need to create additional measurement points in the real-time database, reducing the usage of real-time database measurement point capacity. Define the gateway battery communication link status measurement point R1 in the RDBProxy real-time database driver and the real-time database.

[0027] Step 2: Read and analyze data in RDBEquation. First, read the system time to obtain the current day's 00:00 time and the 00:00 time of the previous 7 days. Read the historical data for 7 days in a 1-day cycle to obtain the first and last data entries for each day. Write the first data entry L1, L2…L7 of each day into A1, A2…A7 respectively; write the last data entry K1, K2…K7 of each day into B1, B2…B7 respectively; then, according to C… i =(K i -L i The daily calculated electricity consumption is written to C1, C2...C7 respectively. The meter's base code is generally updated every 15 minutes, and there may be a delay in writing the data. The snapshots S1 and S2 of the meter's measurement points are read, and the corresponding snapshot times T1 and T2 are obtained. Then, T1 and T2 are compared with the current time T0 to determine the communication link status.

[0028] Step 3: Draw the data monitoring page in Graph3. The top displays detailed base code data and statistical results for the past 7 days in a table format; the lower left is a trend control associated with measurement point R1 to monitor the real-time communication link status; the lower right is a bar chart control associated with measurement points C1, C2...C7 to display the actual power generation situation within 7 days.

[0029] Example

[0030] Taking the statistical analysis of power generation data for generators #1 and #2 at Fuzhou Power Plant as an example, step one: Define the base code measurement point GKDL.AI001 for the power meter of generator #1 in RDBProxy, and define the starting base code measurement points (FZ.UH.N1UC_FDJDM_1DayBefore_BeginTime, FZ.UH.N1UC_FDJDM_2DayBefore_BeginTime, ...) for the power meter of generator #1.

[0031] FZ.UH.N1UC_FDJDM_3DayBefore_BeginTime、

[0032] FZ.UH.N1UC_FDJDM_4DayBefore_BeginTime、

[0033] FZ.UH.N1UC_FDJDM_5DayBefore_BeginTime、

[0034] FZ.UH.N1UC_FDJDM_6DayBefore_BeginTime、

[0035] FZ.UH.N1UC_FDJDM_7DayBefore_EndTime), defines the end-of-day measurement point of the generator #1's power meter (FZ.UH.N1UC_FDJDM_1DayBefore_EndTime, FZ.UH.N1UC_FDJDM_2DayBefore_EndTime).

[0036] FZ.UH.N1UC_FDJDM_3DayBefore_EndTime、

[0037] FZ.UH.N1UC_FDJDM_4DayBefore_EndTime、

[0038] FZ.UH.N1UC_FDJDM_5DayBefore_EndTime、

[0039] FZ.UH.N1UC_FDJDM_6DayBefore_EndTime、

[0040] FZ.UH.N1UC_FDJDM_7DayBefore_EndTime), defines the measurement point for the power generation statistics of generator #1 (FZ.UH.N1UC_W_D_Tol_1DayBefore,

[0041] FZ.UH.N1UC_W_D_Tol_2DayBefore, FZ.UH.N1UC_W_D_Tol_3DayBefore, FZ.UH.N1UC_W_D_Tol_4DayBefore, FZ.UH.N1UC_W_D_Tol_5DayBefore, FZ.UH.N1UC_W_D_Tol_6DayBefore, FZ.UH.N1UC_W_D_Tol_7DayBefore); Define the base code measurement point GKDL.AI002 for the generator #2 power meter, and define the starting base code measurement point (FZ.UH.N2UC_FDJDM_1DayBefore_BeginTime, FZ.UH.N2UC_FDJDM_2DayBefore) for the generator #2 power meter. _BeginTime, FZ.UH.N2UC_FDJDM_3DayBefore_BeginTime, FZ.UH.N2UC_FDJDM_4DayBefore_BeginTime, FZ.UH.N2UC_FDJDM_5DayBefore_BeginTime, FZ.UH.N2UC_FDJDM_6DayBefore_BeginTime, FZ.UH.N2UC_FDJDM_7DayBefore_EndTime), define the end-of-day measurement points for generator #2's power meter (FZ.UH.N2UC_FDJDM_1DayBefore_EndTime, FZ.UH.N2UC_FDJDM_2DayBefore_EndTime),

[0042] FZ.UH.N2UC_FDJDM_3DayBefore_EndTime、

[0043] FZ.UH.N2UC_FDJDM_4DayBefore_EndTime、

[0044] FZ.UH.N2UC_FDJDM_5DayBefore_EndTime、

[0045] FZ.UH.N2UC_FDJDM_6DayBefore_EndTime、

[0046] FZ.UH.N2UC_FDJDM_7DayBefore_EndTime), defines the measurement point for the power generation statistics of generator #2 (FZ.UH.N2UC_W_D_Tol_1DayBefore, ...

[0047] FZ.UH.N2UC_W_D_Tol_2DayBefore, FZ.UH.N2UC_W_D_Tol_3DayBefore, FZ.UH.N2UC_W_D_Tol_4DayBefore, FZ.UH.N2UC_W_D_Tol_5DayBefore, FZ.UH.N2UC_W_D_Tol_6DayBefore, FZ.UH.N2UC_W_D_Tol_7DayBefore); Define the gate power link state measurement point FZ.PT.NPPC.GKDL_LinkState.

[0048] Step Two: First, obtain the current system time, 19:45:29 on July 6, 2023, from RDBEquation. Then, calculate the midnight time T0 (00:00:00 on July 6, 2023). Using T0 as the base and a period of 1 day, extrapolate backwards by 7 days to obtain a time series (00:00:00 on July 5, 2023, 00:00:00 on July 4, 2023, ..., 00:00:00 on June 29, 2023). Then, use the time intervals (00:00:00 on July 5, 2023, 00:00:00 on July 6, 2023), (00:00:00 on July 4, 2023, 00:00:00 on July 5, 2023, 00:00:00 on July 5, 2023) ... (00:00:00 on June 29, 2023) to represent the time intervals (00:00:00 on July 5, 2023, 00:00:00 on July 6 ...

[0049] Using 00:00:00, 2023-06-30 00:00:00 as the statistical period, historical data from the 7-day electricity meter's base code is read to obtain data details and statistical results for each period, such as generator #1 (2023-06-29).

[0050] 00:01:00 Bottom code 118, 2023-06-30 00:00:00 Bottom code 140, 2023-07-01 00:00:00 Bottom code 151, 2023-07-02 00:00:00 Bottom code 153, 2023-07-03 00:00:00 Bottom code 160, 2023-07-04 00:00:00 Bottom code 175, 2023-07-05 00:00:00 Bottom code 188, 2023-07-06 Generator #2 (00:00:00 base code 218); Power generation: 1.32 million kWh on June 29, 2023; 660,000 kWh on June 30, 2023; 120,000 kWh on July 1, 2023; 420,000 kWh on July 2, 2023; 900,000 kWh on July 3, 2023; 780,000 kWh on July 4, 2023; 1.8 million kWh on July 5, 2023); Generator #2 (00:00:00 base code 42 on June 29, 2023; 00:00:00 base code 57 on June 30, 2023; 00:00:00 base code 57 on July 1, 2023).

[0051] 00:00:00 Bottom code 75, 2023-07-02 00:00:00 Bottom code 97, 2023-07-03 00:01:05 Bottom code 111, 2023-07-04 00:00:00 Bottom code 121, 2023-07-05 00:00:00 Base code 140, 2023-07-06 00:00:00 Base code 160; Power generation on 2023-06-29: 900,000 kWh; Power generation on 2023-06-30: 1,080,000 kWh; Power generation on 2023-07-01: 1,320,000 kWh; Power generation on 2023-07-02: 840,000 kWh; Power generation on 2023-07-03: 600,000 kWh; Power generation on 2023-07-04: 1,140,000 kWh; Power generation on 2023-07-05: 1,200,000 kWh. Note that the starting base code time for generator #1 on June 29, 2023, was not exactly 00:00, and the starting time for generator #2 on July 3, 2023, was not exactly 00:00. These two times are displayed in red flashing, indicating that the base code data corresponding to these times is questionable and requires user attention.

[0052] Step 3: The top area displays detailed base code data and statistical results for the past 7 days in a table format, including start time, start base code, end time, end base code, and power generation. The lower left corner uses a trend control to display communication link status monitoring; the vertical axis represents communication status, with a value of 1 indicating normal communication and a value of 0 indicating communication interruption. The horizontal axis is the time axis, displaying data from the previous 7 days to the current system time. Figure 2As shown, a bar chart control is used to display a weekly electricity generation comparison. The vertical axis represents the power generation data, and the horizontal axis is the time axis, displaying the data from the previous 7 days to the previous day of the system. Different bar charts represent the power generation data of different generating units and are displayed in ascending order of time, intuitively reflecting the actual power generation situation within these 7 days.

[0053]

[0054] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A method for monitoring power data based on Graph3, characterized in that, Includes the following steps: Step 1: Define a set of power meter start base code measurement points A1, A2...A7, a set of power meter end base code measurement points B1, B2...B7, and a set of power meter statistical result measurement points C1, C2...C7 for each power indicator, to monitor detailed power information within 7 days before 0:00 on the current day; define a gateway power communication link status measurement point R1 to monitor changes in link status. Step 2: Using 0:00 T0 as the baseline, calculate backwards for 7 days to obtain a time series T1, T2...T7; using (T1, T0), (T2, T1)...(T7, T6) as statistical periods, read the historical data of the 7-day electricity meter readings, and then obtain the first data (L1, L2...L7) and the last data (K1, K2...K7) for each time period. Write (L1, L2...L7) as a data snapshot to (A1, A2...A7), and write (K1, K2...K7) as a data snapshot to (B1, B2...B7). The actual electricity consumption calculation expression is C. i =(K i -L i )*F, where K represents the last data in the statistical period, L represents the first data in the statistical period, the subscript i takes values ​​of 1, 2, ..., 7 to represent the day, F is the electricity meter multiplier, C i The unit is 10,000 kWh; Step 3: Obtain snapshots S1 and S2 of the power meter measurement points to obtain snapshot times M1 and M2; then compare the snapshot times M1 and M2 with the current system time M0. If the deviation between the two is within N0, where N0 is the set time deviation, it is determined that the power link status at the gate is normal; otherwise, it is determined that the power link status at the gate is interrupted. Step 4: The Graph3 display page shows a weekly electricity data monitoring area, used to display electricity details for the past 7 days. It compares the meter reading data with the data displayed on the page to verify if the collected or meter reading data is incorrect. If the meter reading time for a certain day is not 00:00:00, the meter reading data and statistical results for that time are considered questionable, and the time is displayed in flashing red to draw attention. Through comparison, it is determined whether the collected and calculated data matches the actual situation, and then modifications are made according to the actual situation to ensure the accuracy of the data source referenced by reports and other functional modules.

2. The power data monitoring method based on Graph3 according to claim 1, characterized in that, In step 2, the current system time T is first obtained, and then the time T0 at 0:00 on the current day is calculated. The time series T1, T2...T7 is obtained by extrapolating 7 days backward with a period of 1 day. The statistical periods are (T1, T0), (T2, T1)...(T7, T6) respectively. The function of retrieving historical data is called to read the sample values ​​in each time period.

3. The power data monitoring method based on Graph3 according to claim 2, characterized in that, The first sample value within each time period is written to the snapshot of the corresponding measurement point, with the time corresponding to the sample value as the snapshot time. Similarly, the last sample value within each time period is written to the snapshot of the corresponding measurement point, with the time corresponding to the sample value as the snapshot time.

4. The power data monitoring method based on Graph3 according to claim 3, characterized in that, Based on the calculation formula for actual electricity consumption, the data for each statistical period is calculated. The snapshot time is taken by subtracting 1 second from the end time of the statistics and written into the snapshot of the corresponding measurement point.

5. The power data monitoring method based on Graph3 according to claim 1, characterized in that, In step 3, the judgment rules are as follows: R1 = (M1-M0) < N0 or (M2-M0) < N0, where N0 is the set time deviation, and the final judgment status is written into the real-time database.

6. The power data monitoring method based on Graph3 according to claim 1, characterized in that, In step 4, the link status monitoring area is used to track changes in the link status from the previous 7 days to the current time.

7. The power data monitoring method based on Graph3 according to claim 6, characterized in that, By observing changes in trends, we can make a preliminary judgment on the validity of electricity data within 7 days, and also identify instances of data collection interruptions, so as to remind on-site personnel to handle them in a timely manner, restore communication, and ensure the continuity of data collection.

8. The power data monitoring method based on Graph3 according to claim 1, characterized in that, In step 4, the weekly power generation comparison area uses a bar chart to display the power generation statistics of the two units within 7 days in ascending order of time, which can intuitively reflect the actual power generation situation within these 7 days.

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